CHAPTER 8: FORECASTING STRATEGIES

I: Training with single training and testing partitions

1: Training approaches

library(TSstudio)
## Warning: package 'TSstudio' was built under R version 4.3.3
data(USgas)

ts_info(USgas)
##  The USgas series is a ts object with 1 variable and 238 observations
##  Frequency: 12 
##  Start time: 2000 1 
##  End time: 2019 10
train <- window(USgas, 
                start = time(USgas)[1], 
                end = time(USgas)[length(USgas) - 12])

test <- window(USgas, 
               start = time(USgas)[length(USgas) - 12 + 1], 
               end = time(USgas)[length(USgas)])
ts_info(train)
##  The train series is a ts object with 1 variable and 226 observations
##  Frequency: 12 
##  Start time: 2000 1 
##  End time: 2018 10
ts_info(test)
##  The test series is a ts object with 1 variable and 12 observations
##  Frequency: 12 
##  Start time: 2018 11 
##  End time: 2019 10

2: Forecasting with backtesting

USgas_partitions <- ts_split(USgas, sample.out = 12)

train <- USgas_partitions$train
test <- USgas_partitions$test
ts_info(train)
##  The train series is a ts object with 1 variable and 226 observations
##  Frequency: 12 
##  Start time: 2000 1 
##  End time: 2018 10
ts_info(test)
##  The test series is a ts object with 1 variable and 12 observations
##  Frequency: 12 
##  Start time: 2018 11 
##  End time: 2019 10

II: Forecasting evaluation

1: Residual analysis

library(forecast)
## Warning: package 'forecast' was built under R version 4.3.3
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
md <- auto.arima(train)
checkresiduals(md)

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(2,1,1)(2,1,1)[12]
## Q* = 24.95, df = 18, p-value = 0.1263
## 
## Model df: 6.   Total lags used: 24

2: Scoring the forecast

fc <- forecast(md, h = 12)
accuracy(fc, test)
##                     ME      RMSE      MAE       MPE     MAPE      MASE
## Training set  5.844136  97.81626 73.42657 0.1170672 3.522348 0.6376860
## Test set     37.847885 103.22848 81.46603 1.3107987 3.261643 0.7075062
##                      ACF1 Theil's U
## Training set -0.004183172        NA
## Test set     -0.046708926 0.3404092

3: Forecast bencharmk

test_forecast(actual = USgas,
              forecast.obj = fc,
              test = test) 

4

naive_model <- naive(train, h  = 12)
test_forecast(actual = USgas,
              forecast.obj = naive_model,
              test = test)
accuracy(naive_model, test)
##                      ME     RMSE      MAE        MPE     MAPE     MASE
## Training set  -1.028444 285.6607 228.5084 -0.9218463 10.97123 1.984522
## Test set     301.891667 499.6914 379.1417  9.6798015 13.28187 3.292723
##                   ACF1 Theil's U
## Training set 0.3761105        NA
## Test set     0.7002486  1.499679

5

snaive_model <- snaive(train, h = 12)
test_forecast(actual = USgas,
              forecast.obj = snaive_model,
              test = test)
accuracy(snaive_model, test)
##                    ME     RMSE      MAE      MPE     MAPE     MASE       ACF1
## Training set 33.99953 148.7049 115.1453 1.379869 5.494048 1.000000  0.4859501
## Test set     96.45000 164.6967 135.8833 3.612060 5.220458 1.180103 -0.2120929
##              Theil's U
## Training set        NA
## Test set     0.4289964

III: Finalizing the forecast

1: Handling forcast uncertainty

md_final <- auto.arima(USgas)

fc_final <- forecast(md_final, h = 12)

plot_forecast(fc_final,
              title = "The US Natural Gas Consumption Forecast",
              Xtitle = "Year",
              Ytitle = "Billion Cubic Feet")

2: Simulation

fc_final2 <- forecast(md_final, 
                      h = 60, 
                      level = c(80, 90))

plot_forecast(fc_final2,
              title = "The US Natural Gas Consumption Forecast",
              Xtitle = "Year",
              Ytitle = "Billion Cubic Feet")

3: Horse race approach

fc_final3 <- forecast_sim(model = md_final,
                          h = 60, 
                          n = 500) 
library(plotly)
## Warning: package 'plotly' was built under R version 4.3.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.3.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
fc_final3$plot %>% 
  layout(title = "US Natural Gas Consumption - Forecasting Simulation",
         yaxis = list(title = "Billion Cubic Feet"),
         xaxis = list(title = "Year"))

4

# set.seed(1234)
# 
# library(TSstudio)
# 
# # Sample data (replace with your actual time series data)
# data(USgas)

# Backtesting with train_model (corrected arguments)
#USgas_forecast <- train_model(USgas,
#                              method = "ETS",  # Specify the forecasting method (e.g., ETS)
#                              h = 60,           # Specify the forecast horizon (number of steps)
#                              error = "MAPE")  # Specify the error metric (e.g., MAPE)
# 
# # Access forecast results
# forecast <- USgas_forecast$forecast
# 
# # Print summary statistics (optional)
# summary(forecast)  # View basic descriptive statistics
# 
# # Plotting options (see previous response for details)
# 
# 
# # Plotting options:
# 
# # Option 1: Using autoplot (recommended)
# library(ggplot2)  # Load ggplot2 for advanced plotting
# 
# autoplot(forecast)